Hi!

I’m a data scientist in industry, with a background in social science research. I am deeply interested in programming, sociology, the data science industry (and making it better), and education.


Contact me on twitter if you…

  • need advice about getting into data science, especially if you identify as a woman or gender nonbinary
  • need a speaker to come talk at your conference about something I know
  • just want to talk about cool data stuff!

To see more about what kinds of data science I do, check out my projects on this page, or my Github profile.



Events and Fun Stuff


You can see my talk about integrating Elasticsearch storage with R or Python analytics workflows on May 24 in NYC at Rev!


If you missed my keynote at the very first annual SatRdays Chicago Event on April 27, you can watch the whole talk here, alongside all the other excellent talks! To see my slides close up, head over to my github.


If you came to see me speak about R packages for team collaboration, you can get the slides and supporting materials on Github. If you have questions or need help producing your own packages, hit me up on twitter.



Projects


Radlibs! in R or in Python

I wrote a silly R package called radlibs that allows you to make your own madlibs. Then I wrote a version in Python. Then I added them to CRAN and pypi. Data science doesn’t always have to be serious. Use install.packages("radlibs") or pip install radlibs to get these packages. Issues and feedback welcome!



Evaluation of R Forwards Package Workshop

I recently co-taught a daylong course for a group of 30 women/gender nonbinary students about how to write R packages- we had a really good time! I analyzed our pre- and post- surveys in a notebook, to check how effective the day was for students.



Kiva Loan Data Analysis

  • GIS analysis of loans by country with attention to economic conditions in countries
  • Drilldown on some of the thematic areas of the loans
  • Data munging of the regional data provided
  • Exploring modeling potential- if repayment time can be predicted, or anomaly detection if labeling outcomes is not possible



Fun with Real Estate Data

This project is a kaggle kernel, in which I walked the reader through the process of cleaning and modeling the data from a real estate prices dataset, using linear modeling, random forests, and gradient boosting (xgboost). My most popular kernel to date! This one also produced respectable competition results, and was chosen for special recognition by the Kaggle admins. (I won a mug!)

Update: Read the interview I did regarding this project (and the other fabulous winners)! http://blog.kaggle.com/2017/03/29/predicting-house-prices-playground-competition-winning-kernels

Key Skills: machine learning, data cleaning



Data for Democracy 2017 Hackathon

I led a team working on the Chicago Lobbying project, which produced some great output, including this visualization of lobbying and aldermen in Chicago. The project is continuing and building out new functionality. I personally cleaned some of the data underlying, but my biggest contribution was organizing, planning, and leadership. Additional results: https://data.world/lilianhj/chicago-lobbyists

Update: Check out a case study by the fine folks at data.world discussing the work that went in to this project: https://medium.com/@sharonbrener/dbf30aeee70b



Exploring Austin, Texas Crime

Among the public datasets available on Kaggle is this one, describing the crimes that have occurred in Austin, TX over a couple of years. This project cleans the data, does some exploratory analysis, and maps various kinds of crime by district

Key Skills: data cleaning, GIS


See more projects





Kaggle | Twitter | Github | Linkedin

See what I’m reading on Pocket: http://getpocket.com/@data_stephanie

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